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   "outputs": [
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>会员卡号</th>\n",
       "      <th>入会时间</th>\n",
       "      <th>第一次飞行时间</th>\n",
       "      <th>性别</th>\n",
       "      <th>会员卡级别</th>\n",
       "      <th>工作地城市</th>\n",
       "      <th>工作地所在省份</th>\n",
       "      <th>工作地所在国家</th>\n",
       "      <th>年龄</th>\n",
       "      <th>飞行次数</th>\n",
       "      <th>平均折扣系数</th>\n",
       "      <th>最后一次乘机距今天数</th>\n",
       "      <th>飞行里程数</th>\n",
       "      <th>最大乘机时间间隔</th>\n",
       "      <th>总基本积分</th>\n",
       "      <th>总累计积分</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21189</td>\n",
       "      <td>2008-08-22</td>\n",
       "      <td>2008-08-23</td>\n",
       "      <td>男</td>\n",
       "      <td>5</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>US</td>\n",
       "      <td>64.0</td>\n",
       "      <td>23</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>97</td>\n",
       "      <td>281336</td>\n",
       "      <td>73</td>\n",
       "      <td>337314</td>\n",
       "      <td>372204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>39546</td>\n",
       "      <td>2009-04-10</td>\n",
       "      <td>2009-04-15</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>贵阳</td>\n",
       "      <td>贵州</td>\n",
       "      <td>CN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>152</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>5</td>\n",
       "      <td>309928</td>\n",
       "      <td>47</td>\n",
       "      <td>273844</td>\n",
       "      <td>338813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>56972</td>\n",
       "      <td>2008-02-10</td>\n",
       "      <td>2009-09-29</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>64.0</td>\n",
       "      <td>92</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>79</td>\n",
       "      <td>294585</td>\n",
       "      <td>52</td>\n",
       "      <td>313338</td>\n",
       "      <td>343121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>44924</td>\n",
       "      <td>2006-03-22</td>\n",
       "      <td>2006-03-29</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>乌鲁木齐市</td>\n",
       "      <td>新疆</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>101</td>\n",
       "      <td>12.625000</td>\n",
       "      <td>1</td>\n",
       "      <td>287042</td>\n",
       "      <td>28</td>\n",
       "      <td>248864</td>\n",
       "      <td>298873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22631</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>2010-04-09</td>\n",
       "      <td>女</td>\n",
       "      <td>6</td>\n",
       "      <td>温州市</td>\n",
       "      <td>浙江</td>\n",
       "      <td>CN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>73</td>\n",
       "      <td>9.125000</td>\n",
       "      <td>3</td>\n",
       "      <td>287230</td>\n",
       "      <td>45</td>\n",
       "      <td>301864</td>\n",
       "      <td>351198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>31645</td>\n",
       "      <td>2010-07-05</td>\n",
       "      <td>2010-07-05</td>\n",
       "      <td>女</td>\n",
       "      <td>6</td>\n",
       "      <td>温州</td>\n",
       "      <td>浙江</td>\n",
       "      <td>CN</td>\n",
       "      <td>43.0</td>\n",
       "      <td>64</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>15</td>\n",
       "      <td>375074</td>\n",
       "      <td>73</td>\n",
       "      <td>204855</td>\n",
       "      <td>251907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>58877</td>\n",
       "      <td>2010-11-18</td>\n",
       "      <td>2010-11-20</td>\n",
       "      <td>女</td>\n",
       "      <td>6</td>\n",
       "      <td>PARIS</td>\n",
       "      <td>PARIS</td>\n",
       "      <td>FR</td>\n",
       "      <td>34.0</td>\n",
       "      <td>43</td>\n",
       "      <td>5.375000</td>\n",
       "      <td>22</td>\n",
       "      <td>262013</td>\n",
       "      <td>95</td>\n",
       "      <td>298321</td>\n",
       "      <td>337839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>28012</td>\n",
       "      <td>2006-11-23</td>\n",
       "      <td>2007-11-18</td>\n",
       "      <td>男</td>\n",
       "      <td>5</td>\n",
       "      <td>SAN MARINO</td>\n",
       "      <td>CA</td>\n",
       "      <td>US</td>\n",
       "      <td>58.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>67</td>\n",
       "      <td>321529</td>\n",
       "      <td>112</td>\n",
       "      <td>210269</td>\n",
       "      <td>245808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>54943</td>\n",
       "      <td>2006-10-25</td>\n",
       "      <td>2007-10-27</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>118</td>\n",
       "      <td>14.750000</td>\n",
       "      <td>3</td>\n",
       "      <td>179514</td>\n",
       "      <td>38</td>\n",
       "      <td>241614</td>\n",
       "      <td>270704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>57881</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>2010-02-01</td>\n",
       "      <td>女</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50</td>\n",
       "      <td>6.250000</td>\n",
       "      <td>2</td>\n",
       "      <td>270067</td>\n",
       "      <td>100</td>\n",
       "      <td>289917</td>\n",
       "      <td>328345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1254</td>\n",
       "      <td>2008-03-28</td>\n",
       "      <td>2008-04-05</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>BOWLAND HEIGHTS</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>US</td>\n",
       "      <td>63.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>65</td>\n",
       "      <td>234721</td>\n",
       "      <td>102</td>\n",
       "      <td>286164</td>\n",
       "      <td>310002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8253</td>\n",
       "      <td>2010-07-15</td>\n",
       "      <td>2010-08-20</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>乌鲁木齐</td>\n",
       "      <td>新疆</td>\n",
       "      <td>CN</td>\n",
       "      <td>48.0</td>\n",
       "      <td>101</td>\n",
       "      <td>12.625000</td>\n",
       "      <td>7</td>\n",
       "      <td>172231</td>\n",
       "      <td>41</td>\n",
       "      <td>219995</td>\n",
       "      <td>257193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>26955</td>\n",
       "      <td>2006-04-06</td>\n",
       "      <td>2007-02-22</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>乌鲁木齐市</td>\n",
       "      <td>新疆</td>\n",
       "      <td>CN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>64</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>169358</td>\n",
       "      <td>48</td>\n",
       "      <td>215013</td>\n",
       "      <td>249914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>41616</td>\n",
       "      <td>2011-08-29</td>\n",
       "      <td>2011-10-22</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>东莞</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>38</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>24</td>\n",
       "      <td>332896</td>\n",
       "      <td>74</td>\n",
       "      <td>191038</td>\n",
       "      <td>215694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>41281</td>\n",
       "      <td>2011-06-07</td>\n",
       "      <td>2011-06-09</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>VECHEL</td>\n",
       "      <td>NORD BRABANT</td>\n",
       "      <td>AN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>6</td>\n",
       "      <td>214590</td>\n",
       "      <td>135</td>\n",
       "      <td>255573</td>\n",
       "      <td>286520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>47229</td>\n",
       "      <td>2005-04-10</td>\n",
       "      <td>2005-04-10</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>94</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>23</td>\n",
       "      <td>305250</td>\n",
       "      <td>42</td>\n",
       "      <td>193169</td>\n",
       "      <td>277394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13942</td>\n",
       "      <td>2010-10-14</td>\n",
       "      <td>2010-11-01</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>PARIS</td>\n",
       "      <td>FRANCE</td>\n",
       "      <td>FR</td>\n",
       "      <td>39.0</td>\n",
       "      <td>62</td>\n",
       "      <td>7.750000</td>\n",
       "      <td>17</td>\n",
       "      <td>243674</td>\n",
       "      <td>121</td>\n",
       "      <td>241719</td>\n",
       "      <td>274882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>45075</td>\n",
       "      <td>2007-02-01</td>\n",
       "      <td>2007-03-23</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>湛江</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>213</td>\n",
       "      <td>26.625000</td>\n",
       "      <td>3</td>\n",
       "      <td>187917</td>\n",
       "      <td>37</td>\n",
       "      <td>217809</td>\n",
       "      <td>795398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>54619</td>\n",
       "      <td>2006-01-07</td>\n",
       "      <td>2006-01-08</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>62.0</td>\n",
       "      <td>101</td>\n",
       "      <td>12.625000</td>\n",
       "      <td>18</td>\n",
       "      <td>159129</td>\n",
       "      <td>29</td>\n",
       "      <td>209362</td>\n",
       "      <td>237787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12349</td>\n",
       "      <td>2008-06-16</td>\n",
       "      <td>2008-06-27</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>87</td>\n",
       "      <td>10.875000</td>\n",
       "      <td>11</td>\n",
       "      <td>149254</td>\n",
       "      <td>36</td>\n",
       "      <td>199866</td>\n",
       "      <td>224081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>35883</td>\n",
       "      <td>2006-04-11</td>\n",
       "      <td>2007-04-18</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>乌鲁木齐市</td>\n",
       "      <td>新疆</td>\n",
       "      <td>CN</td>\n",
       "      <td>55.0</td>\n",
       "      <td>53</td>\n",
       "      <td>6.625000</td>\n",
       "      <td>2</td>\n",
       "      <td>144076</td>\n",
       "      <td>61</td>\n",
       "      <td>195075</td>\n",
       "      <td>211879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>56091</td>\n",
       "      <td>2004-11-25</td>\n",
       "      <td>2005-02-10</td>\n",
       "      <td>女</td>\n",
       "      <td>6</td>\n",
       "      <td>广州市</td>\n",
       "      <td>广东省</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>95</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>8</td>\n",
       "      <td>149053</td>\n",
       "      <td>80</td>\n",
       "      <td>197222</td>\n",
       "      <td>212131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2137</td>\n",
       "      <td>2005-04-11</td>\n",
       "      <td>2005-05-03</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>69.0</td>\n",
       "      <td>131</td>\n",
       "      <td>16.375000</td>\n",
       "      <td>16</td>\n",
       "      <td>220948</td>\n",
       "      <td>50</td>\n",
       "      <td>199716</td>\n",
       "      <td>234826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>27708</td>\n",
       "      <td>2006-03-20</td>\n",
       "      <td>2006-03-25</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>95</td>\n",
       "      <td>11.875000</td>\n",
       "      <td>3</td>\n",
       "      <td>148227</td>\n",
       "      <td>37</td>\n",
       "      <td>194326</td>\n",
       "      <td>220012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>3539</td>\n",
       "      <td>2009-10-13</td>\n",
       "      <td>2009-10-13</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>UPLAND</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>US</td>\n",
       "      <td>53.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>108</td>\n",
       "      <td>220389</td>\n",
       "      <td>72</td>\n",
       "      <td>228851</td>\n",
       "      <td>258717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>56149</td>\n",
       "      <td>2005-02-13</td>\n",
       "      <td>2005-03-06</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>番禺</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>24</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>34</td>\n",
       "      <td>189813</td>\n",
       "      <td>192</td>\n",
       "      <td>228227</td>\n",
       "      <td>239108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>44936</td>\n",
       "      <td>2006-03-30</td>\n",
       "      <td>2007-05-02</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>乌鲁木齐市</td>\n",
       "      <td>新疆</td>\n",
       "      <td>CN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>54</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>125</td>\n",
       "      <td>141012</td>\n",
       "      <td>54</td>\n",
       "      <td>183336</td>\n",
       "      <td>206363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>62751</td>\n",
       "      <td>2007-05-08</td>\n",
       "      <td>2007-08-12</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>166</td>\n",
       "      <td>20.750000</td>\n",
       "      <td>1</td>\n",
       "      <td>229303</td>\n",
       "      <td>31</td>\n",
       "      <td>206908</td>\n",
       "      <td>260880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>40066</td>\n",
       "      <td>2009-02-27</td>\n",
       "      <td>2009-03-04</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>81</td>\n",
       "      <td>10.125000</td>\n",
       "      <td>34</td>\n",
       "      <td>140819</td>\n",
       "      <td>46</td>\n",
       "      <td>185232</td>\n",
       "      <td>213436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>13963</td>\n",
       "      <td>2011-01-18</td>\n",
       "      <td>2011-04-03</td>\n",
       "      <td>男</td>\n",
       "      <td>6</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>63.0</td>\n",
       "      <td>35</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>3</td>\n",
       "      <td>201141</td>\n",
       "      <td>84</td>\n",
       "      <td>230527</td>\n",
       "      <td>254051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9969</th>\n",
       "      <td>38766</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>2007-11-01</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>广州市</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>2</td>\n",
       "      <td>29977</td>\n",
       "      <td>230</td>\n",
       "      <td>18060</td>\n",
       "      <td>19660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9970</th>\n",
       "      <td>32030</td>\n",
       "      <td>2012-03-09</td>\n",
       "      <td>2012-03-09</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>OTA-SHI</td>\n",
       "      <td>GUMMA-KEN</td>\n",
       "      <td>JP</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>291</td>\n",
       "      <td>23119</td>\n",
       "      <td>195</td>\n",
       "      <td>21160</td>\n",
       "      <td>21160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9971</th>\n",
       "      <td>7064</td>\n",
       "      <td>2012-10-11</td>\n",
       "      <td>2012-10-11</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>番禺</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2.833333</td>\n",
       "      <td>57</td>\n",
       "      <td>26635</td>\n",
       "      <td>170</td>\n",
       "      <td>21822</td>\n",
       "      <td>21822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9972</th>\n",
       "      <td>20699</td>\n",
       "      <td>2007-08-10</td>\n",
       "      <td>2010-03-31</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>13</td>\n",
       "      <td>1.625000</td>\n",
       "      <td>65</td>\n",
       "      <td>24859</td>\n",
       "      <td>201</td>\n",
       "      <td>17137</td>\n",
       "      <td>17137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9973</th>\n",
       "      <td>4892</td>\n",
       "      <td>2010-11-05</td>\n",
       "      <td>2010-11-05</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>郑州</td>\n",
       "      <td>河南</td>\n",
       "      <td>CN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>29</td>\n",
       "      <td>3.625000</td>\n",
       "      <td>70</td>\n",
       "      <td>23263</td>\n",
       "      <td>78</td>\n",
       "      <td>21042</td>\n",
       "      <td>21042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9974</th>\n",
       "      <td>60936</td>\n",
       "      <td>2012-08-11</td>\n",
       "      <td>2012-08-11</td>\n",
       "      <td>女</td>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>270</td>\n",
       "      <td>25479</td>\n",
       "      <td>238</td>\n",
       "      <td>23264</td>\n",
       "      <td>23264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9975</th>\n",
       "      <td>55248</td>\n",
       "      <td>2007-04-26</td>\n",
       "      <td>2007-05-29</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>9</td>\n",
       "      <td>36383</td>\n",
       "      <td>141</td>\n",
       "      <td>14796</td>\n",
       "      <td>14796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9976</th>\n",
       "      <td>33915</td>\n",
       "      <td>2012-09-12</td>\n",
       "      <td>2012-09-12</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>Gwangjin-gu</td>\n",
       "      <td>Seoul</td>\n",
       "      <td>KR</td>\n",
       "      <td>56.0</td>\n",
       "      <td>12</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>220</td>\n",
       "      <td>27864</td>\n",
       "      <td>138</td>\n",
       "      <td>9888</td>\n",
       "      <td>9888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9977</th>\n",
       "      <td>42786</td>\n",
       "      <td>2012-07-11</td>\n",
       "      <td>2012-07-11</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>成都</td>\n",
       "      <td>四川</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>615</td>\n",
       "      <td>27244</td>\n",
       "      <td>13</td>\n",
       "      <td>10292</td>\n",
       "      <td>10292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9978</th>\n",
       "      <td>48749</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>女</td>\n",
       "      <td>4</td>\n",
       "      <td>ALOR STAR</td>\n",
       "      <td>KEDAH DARUL AMAN</td>\n",
       "      <td>MY</td>\n",
       "      <td>51.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>93</td>\n",
       "      <td>29004</td>\n",
       "      <td>104</td>\n",
       "      <td>11552</td>\n",
       "      <td>11552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9979</th>\n",
       "      <td>31004</td>\n",
       "      <td>2009-03-18</td>\n",
       "      <td>2009-05-10</td>\n",
       "      <td>女</td>\n",
       "      <td>4</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>253</td>\n",
       "      <td>26378</td>\n",
       "      <td>91</td>\n",
       "      <td>20273</td>\n",
       "      <td>20273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9980</th>\n",
       "      <td>19570</td>\n",
       "      <td>2010-07-04</td>\n",
       "      <td>2010-07-04</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>平坝</td>\n",
       "      <td>贵州</td>\n",
       "      <td>CN</td>\n",
       "      <td>54.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>43</td>\n",
       "      <td>27922</td>\n",
       "      <td>155</td>\n",
       "      <td>17869</td>\n",
       "      <td>19866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9981</th>\n",
       "      <td>54405</td>\n",
       "      <td>2012-04-12</td>\n",
       "      <td>2012-05-23</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>汕头</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>51.0</td>\n",
       "      <td>28</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>16</td>\n",
       "      <td>31003</td>\n",
       "      <td>187</td>\n",
       "      <td>19915</td>\n",
       "      <td>31676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9982</th>\n",
       "      <td>58622</td>\n",
       "      <td>2010-04-22</td>\n",
       "      <td>2010-04-24</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>深圳</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>39.0</td>\n",
       "      <td>25</td>\n",
       "      <td>3.125000</td>\n",
       "      <td>1</td>\n",
       "      <td>33301</td>\n",
       "      <td>93</td>\n",
       "      <td>14501</td>\n",
       "      <td>15501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>49906</td>\n",
       "      <td>2010-11-11</td>\n",
       "      <td>2010-12-12</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>宁波</td>\n",
       "      <td>浙江</td>\n",
       "      <td>CN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.875000</td>\n",
       "      <td>1</td>\n",
       "      <td>24765</td>\n",
       "      <td>43</td>\n",
       "      <td>17038</td>\n",
       "      <td>17038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9984</th>\n",
       "      <td>56901</td>\n",
       "      <td>2008-03-09</td>\n",
       "      <td>2008-03-09</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>26</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>42</td>\n",
       "      <td>32177</td>\n",
       "      <td>177</td>\n",
       "      <td>14045</td>\n",
       "      <td>14145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>21974</td>\n",
       "      <td>2011-03-05</td>\n",
       "      <td>2011-03-10</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>晋城</td>\n",
       "      <td>山西</td>\n",
       "      <td>CN</td>\n",
       "      <td>35.0</td>\n",
       "      <td>28</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>19</td>\n",
       "      <td>25229</td>\n",
       "      <td>170</td>\n",
       "      <td>17827</td>\n",
       "      <td>17827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9986</th>\n",
       "      <td>9578</td>\n",
       "      <td>2006-03-21</td>\n",
       "      <td>2006-04-25</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>77.0</td>\n",
       "      <td>22</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>94</td>\n",
       "      <td>35085</td>\n",
       "      <td>92</td>\n",
       "      <td>13856</td>\n",
       "      <td>13856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9987</th>\n",
       "      <td>61964</td>\n",
       "      <td>2012-11-29</td>\n",
       "      <td>2012-11-30</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>沈阳市</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>CN</td>\n",
       "      <td>46.0</td>\n",
       "      <td>13</td>\n",
       "      <td>2.166667</td>\n",
       "      <td>11</td>\n",
       "      <td>24717</td>\n",
       "      <td>127</td>\n",
       "      <td>17398</td>\n",
       "      <td>17398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9988</th>\n",
       "      <td>51622</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>男</td>\n",
       "      <td>5</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>CN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>28</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>76</td>\n",
       "      <td>34627</td>\n",
       "      <td>83</td>\n",
       "      <td>15330</td>\n",
       "      <td>15330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9989</th>\n",
       "      <td>52693</td>\n",
       "      <td>2010-02-03</td>\n",
       "      <td>2010-03-09</td>\n",
       "      <td>男</td>\n",
       "      <td>5</td>\n",
       "      <td>长沙</td>\n",
       "      <td>湖南</td>\n",
       "      <td>CN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>27</td>\n",
       "      <td>3.375000</td>\n",
       "      <td>7</td>\n",
       "      <td>22966</td>\n",
       "      <td>80</td>\n",
       "      <td>19453</td>\n",
       "      <td>19453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9990</th>\n",
       "      <td>5214</td>\n",
       "      <td>2011-08-01</td>\n",
       "      <td>2011-08-13</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>南京</td>\n",
       "      <td>江苏</td>\n",
       "      <td>CN</td>\n",
       "      <td>42.0</td>\n",
       "      <td>21</td>\n",
       "      <td>2.625000</td>\n",
       "      <td>11</td>\n",
       "      <td>29519</td>\n",
       "      <td>255</td>\n",
       "      <td>17907</td>\n",
       "      <td>32347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9991</th>\n",
       "      <td>22045</td>\n",
       "      <td>2009-02-17</td>\n",
       "      <td>2009-06-29</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>大连</td>\n",
       "      <td>辽宁</td>\n",
       "      <td>CN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>112</td>\n",
       "      <td>31443</td>\n",
       "      <td>100</td>\n",
       "      <td>14610</td>\n",
       "      <td>14610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9992</th>\n",
       "      <td>11264</td>\n",
       "      <td>2005-07-25</td>\n",
       "      <td>2005-07-25</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>CN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>17</td>\n",
       "      <td>2.125000</td>\n",
       "      <td>126</td>\n",
       "      <td>23148</td>\n",
       "      <td>132</td>\n",
       "      <td>17582</td>\n",
       "      <td>17582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9993</th>\n",
       "      <td>9414</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>2012-12-31</td>\n",
       "      <td>女</td>\n",
       "      <td>5</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>29.0</td>\n",
       "      <td>26</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>83</td>\n",
       "      <td>36201</td>\n",
       "      <td>75</td>\n",
       "      <td>14615</td>\n",
       "      <td>24504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>43855</td>\n",
       "      <td>2011-10-28</td>\n",
       "      <td>2011-10-28</td>\n",
       "      <td>女</td>\n",
       "      <td>4</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>CN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>20</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>269</td>\n",
       "      <td>35865</td>\n",
       "      <td>129</td>\n",
       "      <td>14243</td>\n",
       "      <td>14243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9995</th>\n",
       "      <td>27383</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>2012-07-17</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>36.0</td>\n",
       "      <td>31</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>36</td>\n",
       "      <td>22901</td>\n",
       "      <td>128</td>\n",
       "      <td>20072</td>\n",
       "      <td>21270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9996</th>\n",
       "      <td>4799</td>\n",
       "      <td>2010-06-17</td>\n",
       "      <td>2010-08-12</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>上海</td>\n",
       "      <td>上海</td>\n",
       "      <td>CN</td>\n",
       "      <td>50.0</td>\n",
       "      <td>35</td>\n",
       "      <td>4.375000</td>\n",
       "      <td>4</td>\n",
       "      <td>29325</td>\n",
       "      <td>62</td>\n",
       "      <td>18248</td>\n",
       "      <td>18248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9997</th>\n",
       "      <td>47273</td>\n",
       "      <td>2005-05-08</td>\n",
       "      <td>2005-10-11</td>\n",
       "      <td>男</td>\n",
       "      <td>4</td>\n",
       "      <td>广州</td>\n",
       "      <td>广东</td>\n",
       "      <td>CN</td>\n",
       "      <td>40.0</td>\n",
       "      <td>26</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>18</td>\n",
       "      <td>32870</td>\n",
       "      <td>134</td>\n",
       "      <td>13351</td>\n",
       "      <td>14640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9998</th>\n",
       "      <td>603</td>\n",
       "      <td>2006-03-07</td>\n",
       "      <td>2006-04-04</td>\n",
       "      <td>女</td>\n",
       "      <td>4</td>\n",
       "      <td>北京</td>\n",
       "      <td>北京</td>\n",
       "      <td>CN</td>\n",
       "      <td>56.0</td>\n",
       "      <td>14</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>58</td>\n",
       "      <td>21515</td>\n",
       "      <td>261</td>\n",
       "      <td>18283</td>\n",
       "      <td>18283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9999 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       会员卡号       入会时间    第一次飞行时间 性别  会员卡级别            工作地城市  \\\n",
       "0     21189 2008-08-22 2008-08-23  男      5      Los Angeles   \n",
       "1     39546 2009-04-10 2009-04-15  男      6               贵阳   \n",
       "2     56972 2008-02-10 2009-09-29  男      6               广州   \n",
       "3     44924 2006-03-22 2006-03-29  男      6            乌鲁木齐市   \n",
       "4     22631 2010-04-09 2010-04-09  女      6              温州市   \n",
       "5     31645 2010-07-05 2010-07-05  女      6               温州   \n",
       "6     58877 2010-11-18 2010-11-20  女      6            PARIS   \n",
       "7     28012 2006-11-23 2007-11-18  男      5       SAN MARINO   \n",
       "8     54943 2006-10-25 2007-10-27  男      6               深圳   \n",
       "9     57881 2010-02-01 2010-02-01  女      6               广州   \n",
       "10     1254 2008-03-28 2008-04-05  男      4  BOWLAND HEIGHTS   \n",
       "11     8253 2010-07-15 2010-08-20  男      6             乌鲁木齐   \n",
       "12    26955 2006-04-06 2007-02-22  男      6            乌鲁木齐市   \n",
       "13    41616 2011-08-29 2011-10-22  男      6               东莞   \n",
       "14    41281 2011-06-07 2011-06-09  男      6           VECHEL   \n",
       "15    47229 2005-04-10 2005-04-10  男      6               广州   \n",
       "16    13942 2010-10-14 2010-11-01  男      6            PARIS   \n",
       "17    45075 2007-02-01 2007-03-23  男      6               湛江   \n",
       "18    54619 2006-01-07 2006-01-08  男      6               广州   \n",
       "19    12349 2008-06-16 2008-06-27  男      6               深圳   \n",
       "20    35883 2006-04-11 2007-04-18  男      6            乌鲁木齐市   \n",
       "21    56091 2004-11-25 2005-02-10  女      6              广州市   \n",
       "22     2137 2005-04-11 2005-05-03  男      6               广州   \n",
       "23    27708 2006-03-20 2006-03-25  男      6               深圳   \n",
       "24     3539 2009-10-13 2009-10-13  男      4           UPLAND   \n",
       "25    56149 2005-02-13 2005-03-06  男      4               番禺   \n",
       "26    44936 2006-03-30 2007-05-02  男      6            乌鲁木齐市   \n",
       "27    62751 2007-05-08 2007-08-12  男      6               北京   \n",
       "28    40066 2009-02-27 2009-03-04  男      6               广州   \n",
       "29    13963 2011-01-18 2011-04-03  男      6               广州   \n",
       "...     ...        ...        ... ..    ...              ...   \n",
       "9969  38766 2007-11-01 2007-11-01  男      4              广州市   \n",
       "9970  32030 2012-03-09 2012-03-09  男      4          OTA-SHI   \n",
       "9971   7064 2012-10-11 2012-10-11  男      4               番禺   \n",
       "9972  20699 2007-08-10 2010-03-31  男      4               北京   \n",
       "9973   4892 2010-11-05 2010-11-05  男      4               郑州   \n",
       "9974  60936 2012-08-11 2012-08-11  女      4               广州   \n",
       "9975  55248 2007-04-26 2007-05-29  男      4               广州   \n",
       "9976  33915 2012-09-12 2012-09-12  男      4      Gwangjin-gu   \n",
       "9977  42786 2012-07-11 2012-07-11  男      4               成都   \n",
       "9978  48749 2011-01-10 2011-01-10  女      4        ALOR STAR   \n",
       "9979  31004 2009-03-18 2009-05-10  女      4               深圳   \n",
       "9980  19570 2010-07-04 2010-07-04  男      4               平坝   \n",
       "9981  54405 2012-04-12 2012-05-23  男      4               汕头   \n",
       "9982  58622 2010-04-22 2010-04-24  男      4               深圳   \n",
       "9983  49906 2010-11-11 2010-12-12  男      4               宁波   \n",
       "9984  56901 2008-03-09 2008-03-09  男      4               广州   \n",
       "9985  21974 2011-03-05 2011-03-10  男      4               晋城   \n",
       "9986   9578 2006-03-21 2006-04-25  男      4               广州   \n",
       "9987  61964 2012-11-29 2012-11-30  男      4              沈阳市   \n",
       "9988  51622 2012-11-05 2012-11-05  男      5               上海   \n",
       "9989  52693 2010-02-03 2010-03-09  男      5               长沙   \n",
       "9990   5214 2011-08-01 2011-08-13  男      4               南京   \n",
       "9991  22045 2009-02-17 2009-06-29  男      4               大连   \n",
       "9992  11264 2005-07-25 2005-07-25  男      4               上海   \n",
       "9993   9414 2012-12-31 2012-12-31  女      5               广州   \n",
       "9994  43855 2011-10-28 2011-10-28  女      4               上海   \n",
       "9995  27383 2012-01-01 2012-07-17  男      4               北京   \n",
       "9996   4799 2010-06-17 2010-08-12  男      4               上海   \n",
       "9997  47273 2005-05-08 2005-10-11  男      4               广州   \n",
       "9998    603 2006-03-07 2006-04-04  女      4               北京   \n",
       "\n",
       "               工作地所在省份 工作地所在国家    年龄  飞行次数     平均折扣系数  最后一次乘机距今天数   飞行里程数  \\\n",
       "0                   CA      US  64.0    23   2.875000          97  281336   \n",
       "1                   贵州      CN  48.0   152  19.000000           5  309928   \n",
       "2                   广东      CN  64.0    92  11.500000          79  294585   \n",
       "3                   新疆      CN  46.0   101  12.625000           1  287042   \n",
       "4                   浙江      CN  50.0    73   9.125000           3  287230   \n",
       "5                   浙江      CN  43.0    64   8.000000          15  375074   \n",
       "6                PARIS      FR  34.0    43   5.375000          22  262013   \n",
       "7                   CA      US  58.0    29   3.625000          67  321529   \n",
       "8                   广东      CN  47.0   118  14.750000           3  179514   \n",
       "9                   广东      CN  45.0    50   6.250000           2  270067   \n",
       "10          CALIFORNIA      US  63.0    22   2.750000          65  234721   \n",
       "11                  新疆      CN  48.0   101  12.625000           7  172231   \n",
       "12                  新疆      CN  54.0    64   8.000000           2  169358   \n",
       "13                  广东      CN  41.0    38   4.750000          24  332896   \n",
       "14        NORD BRABANT      AN   NaN    23   2.875000           6  214590   \n",
       "15                  广东      CN  69.0    94  11.750000          23  305250   \n",
       "16              FRANCE      FR  39.0    62   7.750000          17  243674   \n",
       "17                  广东      CN  46.0   213  26.625000           3  187917   \n",
       "18                  广东      CN  62.0   101  12.625000          18  159129   \n",
       "19                  广东      CN  46.0    87  10.875000          11  149254   \n",
       "20                  新疆      CN  55.0    53   6.625000           2  144076   \n",
       "21                 广东省      CN  46.0    95  11.875000           8  149053   \n",
       "22                  广东      CN  69.0   131  16.375000          16  220948   \n",
       "23                  广东      CN  45.0    95  11.875000           3  148227   \n",
       "24          CALIFORNIA      US  53.0    25   3.125000         108  220389   \n",
       "25                  广东      CN  39.0    24   3.000000          34  189813   \n",
       "26                  新疆      CN  37.0    54   6.750000         125  141012   \n",
       "27                  北京      CN  45.0   166  20.750000           1  229303   \n",
       "28                  广东      CN  63.0    81  10.125000          34  140819   \n",
       "29                  广东      CN  63.0    35   4.375000           3  201141   \n",
       "...                ...     ...   ...   ...        ...         ...     ...   \n",
       "9969                广东      CN  47.0    17   2.125000           2   29977   \n",
       "9970         GUMMA-KEN      JP   NaN    10   1.250000         291   23119   \n",
       "9971                广东      CN  27.0    17   2.833333          57   26635   \n",
       "9972                北京      CN  42.0    13   1.625000          65   24859   \n",
       "9973                河南      CN  36.0    29   3.625000          70   23263   \n",
       "9974                广东      CN  34.0     3   0.428571         270   25479   \n",
       "9975                广东      CN  47.0    25   3.125000           9   36383   \n",
       "9976             Seoul      KR  56.0    12   1.714286         220   27864   \n",
       "9977                四川      CN  46.0     4   0.571429         615   27244   \n",
       "9978  KEDAH DARUL AMAN      MY  51.0    11   1.375000          93   29004   \n",
       "9979                广东      CN  37.0    17   2.125000         253   26378   \n",
       "9980                贵州      CN  54.0    11   1.375000          43   27922   \n",
       "9981                广东      CN  51.0    28   3.500000          16   31003   \n",
       "9982                广东      CN  39.0    25   3.125000           1   33301   \n",
       "9983                浙江      CN  47.0    15   1.875000           1   24765   \n",
       "9984                广东      CN  36.0    26   3.250000          42   32177   \n",
       "9985                山西      CN  35.0    28   3.500000          19   25229   \n",
       "9986                广东      CN  77.0    22   2.750000          94   35085   \n",
       "9987                辽宁      CN  46.0    13   2.166667          11   24717   \n",
       "9988                上海      CN  45.0    28   4.666667          76   34627   \n",
       "9989                湖南      CN  32.0    27   3.375000           7   22966   \n",
       "9990                江苏      CN  42.0    21   2.625000          11   29519   \n",
       "9991                辽宁      CN   NaN    26   3.250000         112   31443   \n",
       "9992                上海      CN  40.0    17   2.125000         126   23148   \n",
       "9993                广东      CN  29.0    26   4.333333          83   36201   \n",
       "9994                上海      CN  32.0    20   2.500000         269   35865   \n",
       "9995                北京      CN  36.0    31   3.875000          36   22901   \n",
       "9996                上海      CN  50.0    35   4.375000           4   29325   \n",
       "9997                广东      CN  40.0    26   3.250000          18   32870   \n",
       "9998                北京      CN  56.0    14   1.750000          58   21515   \n",
       "\n",
       "      最大乘机时间间隔   总基本积分   总累计积分  \n",
       "0           73  337314  372204  \n",
       "1           47  273844  338813  \n",
       "2           52  313338  343121  \n",
       "3           28  248864  298873  \n",
       "4           45  301864  351198  \n",
       "5           73  204855  251907  \n",
       "6           95  298321  337839  \n",
       "7          112  210269  245808  \n",
       "8           38  241614  270704  \n",
       "9          100  289917  328345  \n",
       "10         102  286164  310002  \n",
       "11          41  219995  257193  \n",
       "12          48  215013  249914  \n",
       "13          74  191038  215694  \n",
       "14         135  255573  286520  \n",
       "15          42  193169  277394  \n",
       "16         121  241719  274882  \n",
       "17          37  217809  795398  \n",
       "18          29  209362  237787  \n",
       "19          36  199866  224081  \n",
       "20          61  195075  211879  \n",
       "21          80  197222  212131  \n",
       "22          50  199716  234826  \n",
       "23          37  194326  220012  \n",
       "24          72  228851  258717  \n",
       "25         192  228227  239108  \n",
       "26          54  183336  206363  \n",
       "27          31  206908  260880  \n",
       "28          46  185232  213436  \n",
       "29          84  230527  254051  \n",
       "...        ...     ...     ...  \n",
       "9969       230   18060   19660  \n",
       "9970       195   21160   21160  \n",
       "9971       170   21822   21822  \n",
       "9972       201   17137   17137  \n",
       "9973        78   21042   21042  \n",
       "9974       238   23264   23264  \n",
       "9975       141   14796   14796  \n",
       "9976       138    9888    9888  \n",
       "9977        13   10292   10292  \n",
       "9978       104   11552   11552  \n",
       "9979        91   20273   20273  \n",
       "9980       155   17869   19866  \n",
       "9981       187   19915   31676  \n",
       "9982        93   14501   15501  \n",
       "9983        43   17038   17038  \n",
       "9984       177   14045   14145  \n",
       "9985       170   17827   17827  \n",
       "9986        92   13856   13856  \n",
       "9987       127   17398   17398  \n",
       "9988        83   15330   15330  \n",
       "9989        80   19453   19453  \n",
       "9990       255   17907   32347  \n",
       "9991       100   14610   14610  \n",
       "9992       132   17582   17582  \n",
       "9993        75   14615   24504  \n",
       "9994       129   14243   14243  \n",
       "9995       128   20072   21270  \n",
       "9996        62   18248   18248  \n",
       "9997       134   13351   14640  \n",
       "9998       261   18283   18283  \n",
       "\n",
       "[9999 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_excel(r'./dataset/航空公司数据.xlsx')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  1、画用户“年龄”和“飞行次数”数据的散点图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['font.family'] = ['sans-serif'] #图形中可以显示负号\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.scatter(\n",
    "    data['年龄'],\n",
    "    data['飞行次数'],\n",
    "    s = 35\n",
    ")\n",
    "plt.title('年龄与飞行次数散点图',fontsize=20)\n",
    "plt.xlabel('乘客年龄')\n",
    "plt.ylabel('飞行次数')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2、绘制会员卡等级分布直方图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae0c5647b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.bar(\n",
    "    data['会员卡号'],\n",
    "    data['会员卡级别'],\n",
    "    color = 'red'\n",
    ")\n",
    "plt.title('会员卡的等级直方图',fontsize=20)\n",
    "plt.xlabel('乘客的会员卡号',fontsize=16)\n",
    "plt.ylabel('会员卡等级',fontsize=16)\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.ylim([0,10])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3、统计每年入会会员人数，并画折线图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "年份\n",
       "2004     119\n",
       "2005     788\n",
       "2006    1168\n",
       "2007    1190\n",
       "2008    1146\n",
       "2009    1000\n",
       "2010    1226\n",
       "2011    1531\n",
       "2012    1560\n",
       "2013     223\n",
       "Name: 年龄, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['年份'] = data['入会时间'].agg(lambda x:x.year)\n",
    "data_f = data.groupby(by='年份')['年龄'].count()\n",
    "data_f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae159dedd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(\n",
    "    data_f.index, # 作为x轴,如果不认，需要在index再加个.values\n",
    "    data_f.values, # 作为y轴\n",
    "    color = 'green' # 颜色代码，可以是十六进制颜色码\n",
    ")\n",
    "plt.title('入会年份折线图',fontsize=16)\n",
    "plt.xlabel('年份',fontsize=16)\n",
    "plt.ylabel('入会人数',fontsize=16)\n",
    "plt.xticks(fontsize=12)\n",
    "plt.yticks(fontsize=12)\n",
    "plt.xticks(rotation=30) # x轴刻度倾斜30度\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4、绘制“年龄”数据箱线图。\n",
    "有缺失值必须处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#处理缺失值\n",
    "data['年龄'].isnull().sum() #统计缺失值\n",
    "data2 = data.dropna(subset=['年龄']) #删除缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae0fcace80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#不允许出现缺失值，用这个图要先处理缺失值\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.boxplot(\n",
    "    data2['年龄'],\n",
    "    labels = ['乘客年龄'],\n",
    "    vert = False, \n",
    ")\n",
    "plt.title('乘客年龄箱线图',fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5、假设“飞龄”为入会时间距今天数，试用等宽分箱对“飞龄”数据分组，并统计各组人数，画饼图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       5037\n",
       "1       4806\n",
       "2       5231\n",
       "3       5921\n",
       "4       4442\n",
       "5       4355\n",
       "6       4219\n",
       "7       5675\n",
       "8       5704\n",
       "9       4509\n",
       "10      5184\n",
       "11      4345\n",
       "12      5906\n",
       "13      3935\n",
       "14      4018\n",
       "15      6267\n",
       "16      4254\n",
       "17      5605\n",
       "18      5995\n",
       "19      5104\n",
       "20      5901\n",
       "21      6403\n",
       "22      6266\n",
       "23      5923\n",
       "24      4620\n",
       "25      6323\n",
       "26      5913\n",
       "27      5509\n",
       "28      4848\n",
       "29      4158\n",
       "        ... \n",
       "9969    5332\n",
       "9970    3742\n",
       "9971    3526\n",
       "9972    5415\n",
       "9973    4232\n",
       "9974    3587\n",
       "9975    5521\n",
       "9976    3555\n",
       "9977    3618\n",
       "9978    4166\n",
       "9979    4829\n",
       "9980    4356\n",
       "9981    3708\n",
       "9982    4429\n",
       "9983    4226\n",
       "9984    5203\n",
       "9985    4112\n",
       "9986    5922\n",
       "9987    3477\n",
       "9988    3501\n",
       "9989    4507\n",
       "9990    3963\n",
       "9991    4858\n",
       "9992    6161\n",
       "9993    3445\n",
       "9994    3875\n",
       "9995    3810\n",
       "9996    4373\n",
       "9997    6239\n",
       "9998    5936\n",
       "Name: 入会时间, Length: 9999, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#提取时间差对象中的数值\n",
    "data_fl.agg(lambda x:x.days)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      5037 days\n",
       "1      4806 days\n",
       "2      5231 days\n",
       "3      5921 days\n",
       "4      4442 days\n",
       "5      4355 days\n",
       "6      4219 days\n",
       "7      5675 days\n",
       "8      5704 days\n",
       "9      4509 days\n",
       "10     5184 days\n",
       "11     4345 days\n",
       "12     5906 days\n",
       "13     3935 days\n",
       "14     4018 days\n",
       "15     6267 days\n",
       "16     4254 days\n",
       "17     5605 days\n",
       "18     5995 days\n",
       "19     5104 days\n",
       "20     5901 days\n",
       "21     6403 days\n",
       "22     6266 days\n",
       "23     5923 days\n",
       "24     4620 days\n",
       "25     6323 days\n",
       "26     5913 days\n",
       "27     5509 days\n",
       "28     4848 days\n",
       "29     4158 days\n",
       "          ...   \n",
       "9969   5332 days\n",
       "9970   3742 days\n",
       "9971   3526 days\n",
       "9972   5415 days\n",
       "9973   4232 days\n",
       "9974   3587 days\n",
       "9975   5521 days\n",
       "9976   3555 days\n",
       "9977   3618 days\n",
       "9978   4166 days\n",
       "9979   4829 days\n",
       "9980   4356 days\n",
       "9981   3708 days\n",
       "9982   4429 days\n",
       "9983   4226 days\n",
       "9984   5203 days\n",
       "9985   4112 days\n",
       "9986   5922 days\n",
       "9987   3477 days\n",
       "9988   3501 days\n",
       "9989   4507 days\n",
       "9990   3963 days\n",
       "9991   4858 days\n",
       "9992   6161 days\n",
       "9993   3445 days\n",
       "9994   3875 days\n",
       "9995   3810 days\n",
       "9996   4373 days\n",
       "9997   6239 days\n",
       "9998   5936 days\n",
       "Name: 入会时间, Length: 9999, dtype: timedelta64[ns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = pd.Timestamp('2022-6-7')\n",
    "data_s = data['入会时间']\n",
    "data_fl = time - data_s\n",
    "data_fl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "飞龄\n",
       "二千下    2471\n",
       "三千下    2216\n",
       "四千下    1763\n",
       "四千中    1912\n",
       "四千上    1589\n",
       "Name: 年龄, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#转换成字符串才能作为饼图的标签\n",
    "data['飞龄'] = pd.cut(data_fl,bins=5,labels=['二千下','三千下','四千下','四千中','四千上'])\n",
    "data_op = data.groupby(by=data['飞龄'])['年龄'].count()\n",
    "data_op"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "plt.pie(\n",
    "    data_op,\n",
    "    labels = data_op.index,\n",
    "    autopct = '%1.1f%%',\n",
    ")\n",
    "plt.title('乘客飞龄饼图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 将上次“航空公司数据.xlsx”实操作业中画的5个图以2行3列的布局一次性画出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae0d4269e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae0db674e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "# 指定子图的具体位置分布\n",
    "charts1 = fig.add_subplot(2,3,1) # 1行2列第1个图\n",
    "charts2 = fig.add_subplot(2,3,2) # 1行2列第2个图\n",
    "charts3 = fig.add_subplot(2,3,3) # 1行2列第3个图\n",
    "charts3 = fig.add_subplot(2,3,4) # 1行2列第4个图\n",
    "charts3 = fig.add_subplot(2,3,5) # 1行2列第5个图\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae1b72eb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,4))\n",
    "charts1 = fig.add_subplot(2,3,1)\n",
    "# 第一个子图的图形代码\n",
    "plt.scatter(\n",
    "    data['年龄'],\n",
    "    data['飞行次数'],\n",
    "    s = 35\n",
    ")\n",
    "plt.title('散点图')\n",
    "charts2 = fig.add_subplot(2,3,2)\n",
    "# 第二个子图的图形代码\n",
    "plt.bar(\n",
    "    data['会员卡号'],\n",
    "    data['会员卡级别'],\n",
    "    color = 'red'\n",
    ")\n",
    "plt.title('直方图')\n",
    "charts3 = fig.add_subplot(2,3,3)\n",
    "# 第三个子图的图形代码\n",
    "plt.plot(\n",
    "    data_f.index, # 作为x轴,如果不认，需要在index再加个.values\n",
    "    data_f.values, # 作为y轴\n",
    "    color = 'green'\n",
    ")\n",
    "plt.title('折线图')\n",
    "charts4 = fig.add_subplot(2,3,4)\n",
    "# 第四个子图的图形代码\n",
    "plt.boxplot(\n",
    "    data2['年龄'],\n",
    "    labels = ['乘客年龄'],\n",
    "    vert = False,\n",
    ")\n",
    "plt.title('箱线图')\n",
    "charts5 = fig.add_subplot(2,3,5)\n",
    "# 第五个子图的图形代码\n",
    "plt.pie(\n",
    "    data_op,\n",
    "    labels = data_op.index,\n",
    "    autopct = '%1.1f%%',\n",
    ")\n",
    "plt.title('饼状图')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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